Class GammaFromShapeAndScaleOp

Provides outgoing messages for Sample(Double, Double), given random arguments to the function.

Inheritance

Object

GammaFromShapeAndScaleOp

Inherited Members

Object.Equals(Object)

Object.Equals(Object, Object)

Object.GetHashCode()

Object.GetType()

Object.MemberwiseClone()

Object.ReferenceEquals(Object, Object)

Object.ToString()

Namespace: Microsoft.ML.Probabilistic.Factors

Assembly: Microsoft.ML.Probabilistic.dll

Syntax

[FactorMethod(typeof(Gamma), "Sample", new Type[]{typeof(double), typeof(double)})]
[Quality(QualityBand.Stable)]
public static class GammaFromShapeAndScaleOp

Methods

AverageLogFactor(Gamma, Double, Double)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(Gamma sample, double shape, double scale)

Parameters

Type	Name	Description
Gamma	sample	Incoming message from sample. Must be a proper distribution. If uniform, the result will be uniform.
Double	shape	Constant value for shape.
Double	scale	Constant value for scale.

Returns

Type	Description
Double	Average of the factor's log-value across the given argument distributions.

Remarks

The formula for the result is sum_(sample) p(sample) log(factor(sample,shape,scale)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

Exceptions

Type	Condition
ImproperMessageException	sample is not a proper distribution.

AverageLogFactor(Double, Double, Double)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(double sample, double shape, double scale)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	shape	Constant value for shape.
Double	scale	Constant value for scale.

Returns

Type	Description
Double	Average of the factor's log-value across the given argument distributions.

Remarks

The formula for the result is log(factor(sample,shape,scale)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

LogAverageFactor(Gamma, Gamma)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Gamma sample, Gamma to_sample)

Parameters

Type	Name	Description
Gamma	sample	Incoming message from sample.
Gamma	to_sample	Outgoing message to sample.

Returns

Type	Description
Double	Logarithm of the factor's average value across the given argument distributions.

Remarks

The formula for the result is log(sum_(sample) p(sample) factor(sample,shape,scale)).

LogAverageFactor(Double, Double, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double sample, double shape, double scale)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	shape	Constant value for shape.
Double	scale	Constant value for scale.

Returns

Type	Description
Double	Logarithm of the factor's average value across the given argument distributions.

Remarks

The formula for the result is log(factor(sample,shape,scale)).

LogEvidenceRatio(Gamma, Double, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Gamma sample, double shape, double scale)

Parameters

Type	Name	Description
Gamma	sample	Incoming message from sample.
Double	shape	Constant value for shape.
Double	scale	Constant value for scale.

Returns

Type	Description
Double	Logarithm of the factor's contribution the EP model evidence.

Remarks

The formula for the result is log(sum_(sample) p(sample) factor(sample,shape,scale) / sum_sample p(sample) messageTo(sample)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Double, Double, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double sample, double shape, double scale)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	shape	Constant value for shape.
Double	scale	Constant value for scale.

Returns

Type	Description
Double	Logarithm of the factor's contribution the EP model evidence.

Remarks

The formula for the result is log(factor(sample,shape,scale)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

SampleAverageConditional(Double, Double)

EP message to sample.

Declaration

public static Gamma SampleAverageConditional(double shape, double scale)

Parameters

Type	Name	Description
Double	shape	Constant value for shape.
Double	scale	Constant value for scale.

Returns

Type	Description
Gamma	The outgoing EP message to the sample argument.

Remarks

The outgoing message is the factor viewed as a function of sample conditioned on the given values.

SampleAverageLogarithm(Double, Double)

VMP message to sample.

Declaration

public static Gamma SampleAverageLogarithm(double shape, double scale)

Parameters

Type	Name	Description
Double	shape	Constant value for shape.
Double	scale	Constant value for scale.

Returns

Type	Description
Gamma	The outgoing VMP message to the sample argument.

Remarks

The outgoing message is the factor viewed as a function of sample conditioned on the given values.